This is the condensed review layer for NCP-AIO: the command tables, models, and decision trees the exam's 30 questions and 3 live lab exercises draw from. Use it for final-week passes and as a fluency checklist (every command here should run from your hands without a lookup, because half this exam is a terminal). Depth lives in the domains breakdown; logistics in the complete guide.
Exam Quick Facts
The Operations Stack Map (Installation, 31%)
| Layer | Component | Owns |
|---|---|---|
| Cluster manager | Base Command Manager (BCM) | Node provisioning from images, software lifecycle, cmsh/Base View |
| Deployment/ops toolkit | Mission Control | Cluster bring-up, validation, and operations workflows at SuperPOD scale |
| Batch scheduler | Slurm | Training queues, partitions, GRES GPU scheduling |
| Service orchestrator | Kubernetes + GPU Operator | Inference and services; operator manages driver, toolkit, device plugin, DCGM exporter |
| GPU orchestration | Run:ai | Projects, quotas, fractional GPUs, over-quota scheduling on top of Kubernetes |
| Registry | NGC | Qualified containers, models, Helm charts (ngc CLI) |
| DPU services | DOCA | Infrastructure services offloaded to BlueField |
Installation order intuition: BCM provisions nodes, schedulers install on nodes, Run:ai installs on Kubernetes, workloads come last.
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Slurm Admin Table (Administration, 23%)
| Task | Command |
|---|---|
| Cluster/node state overview | sinfo |
| Queue state | squeue |
| Node detail (state, GRES, reason) | scontrol show node <name> |
| Drain a node with a reason | scontrol update nodename=<n> state=drain reason="..." |
| Return a drained node | scontrol update nodename=<n> state=resume |
| Request GPUs in a job | sbatch --gres=gpu:2 (declared in gres.conf) |
| Usage accounting | sacct / sacctmgr (accounts, associations, limits) |
| Job control | scancel, scontrol hold/release |
Fair-share mechanics: QoS and fair-share weighting stop one tenant from starving others; accounting associations are where limits attach. Container jobs: Pyxis + Enroot run NGC containers under Slurm (srun --container-image=...).
Run:ai model in three lines: projects map teams to GPU quotas; over-quota scheduling lends idle GPUs and reclaims them when owners return; fractional GPUs pack underutilizing workloads onto shared devices.
Workload Table (Workload Management, 23%)
Serving and Launch Patterns
| Need | Tool/Feature | Key fact |
|---|---|---|
| Throughput on an inference service | Triton dynamic batching | Queues requests briefly to form batches; tune max delay vs latency budget |
| Multiple models / copies per GPU | Triton instance groups + concurrent execution | Parallel model instances on one device |
| Turnkey LLM serving | NIM microservices | Containerized models with standard APIs; fastest path to production |
| High-throughput LLM inference | vLLM | PagedAttention + continuous batching |
| Multi-node training launch | Slurm + Pyxis/Enroot | GRES request + stable rendezvous (master addr, world size) |
| Model files for Triton | Model repository layout | model dir + config.pbtxt + versioned subdirs |
Master These Concepts with Practice
Our NCP-AIO practice bundle includes:
- 7 full practice exams (455+ questions)
- Detailed explanations for every answer
- Domain-by-domain performance tracking
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The Five-Layer Troubleshooting Tree (23%)
Classify the layer first, fix second:
- GPU hardware: Xid/ECC in kernel logs, DCGM alerts → drain, diagnose, replace if confirmed
- Fabric: peer-to-peer jobs fail while single-GPU runs fine → Fabric Manager service,
nvidia-smi nvlinkcounters - Scheduler: job pends forever → quota exhaustion, impossible GRES request, drained nodes
- Container: GPU invisible inside container → toolkit config, runtime class, image pull, permissions
- Storage/network: GPUs idle mid-job → dataloader starvation vs storage throughput vs network congestion; measure in that order
Xid quick table:
| Xid | Meaning | Action class |
|---|---|---|
| 48 | Double-bit ECC error | Drain and diagnose memory health |
| 63 / 64 | Row remapping (HBM) | Monitor; replace if recurring |
| 79 | GPU fell off the bus | Hardware attention: reseat/replace, revalidate |
ECC accounting: volatile counters reset at reboot, aggregate counters persist; recurring row remaps start a replacement conversation. DCGM: health watches for continuous monitoring, dcgmi diag ladder for on-demand depth, policies for automated alerts.
MIG Rules (across domains)
- H100/A100 partition into up to 7 instances; profiles named like
1g.10gb,3g.40gb,7g.80gb - Reconfiguration requires an idle GPU
- Kubernetes device plugin exposes MIG via single strategy (uniform slices) or mixed strategy (heterogeneous slices as distinct resources)
- MIG = hard isolation and predictable QoS; time-slicing = soft sharing for bursty work; vGPU = licensed virtualization for VDI
Exam-Format Facts Worth Cold Recall
- 30 multiple-choice questions plus 3 hands-on lab exercises share one 120-minute session
- MCQ speed buys lab time; target 60-75 seconds per question
- Lab exercises grade end state: verify results the way production would
- Easiest lab first, hard per-lab time cap, partial progress beats one perfect exercise
- $500 per attempt; two-year validity; Certiverse remote proctoring
Final-Week Usage
Run the sheet top to bottom and mark hesitations, then split remediation by type: knowledge gaps go to practice questions, fluency gaps go to lab re-runs. Preporato's NCP-AIO prep covers both sides with 7 full-length practice exams (420 explained questions, per-domain tracking) and 19 hands-on GPU labs. Close with the first-attempt strategy.
Sources:
- NVIDIA NCP-AIO Official Certification Page
- NVIDIA Base Command Manager Documentation
- NVIDIA Triton Inference Server Documentation
- Slurm Workload Manager Documentation
Last updated: July 9, 2026
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